title: Maximum likelihood tomographic reconstruction of extremely sparse solutions in diffuse fluorescence flow cytometry.
abstract: We apply reparameterization and the maximum likelihood method to a specific fluorescence-mediated tomography problem where the solution is known a priori to be extremely sparse (i.e., all image values are zero except for one). Our algorithm performs significantly better than a standard image reconstruction method, particularly for deep-seated targets, and achieves close to 150 μm accuracy in a 3 mm diameter cross-sectional area with only 12 measurements. Moreover, results do not depend on the selection of a regularization parameter or other ad hoc values, and since reconstructions can be computed very quickly, the algorithm is also suitable for real-time implementation.
authors.affiliation: Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115, USA. pera.v@husky.neu.edu
authors.forename: Vivian
authors.initials: V
authors.lastname: Pera
authors.forename: Eric
authors.initials: E
authors.lastname: Zettergren
authors.forename: Dana H
authors.initials: DH
authors.lastname: Brooks
authors.forename: Mark
authors.initials: M
authors.lastname: Niedre
mesh_terms: D005434:Flow Cytometry / Q000379:methods*; D005453:Fluorescence; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D019047:Phantoms, Imaging; D014054:Tomography / Q000379:methods*